Fibrosis is a significant indication of chronic liver diseases often due to hepatitis C Virus. It is becoming a global concern as a result of the rapid increase in the number of HCV infected patients, the high cost and flaws associated with the assessment process of liver fibrosis. This study aims to determine the features that significantly contribute to the identification of the stages of liver fibrosis and to generate rules to assist physicians during the treatment of the patients as a clinically non-invasive approach. Also, the performance of different Multi-layered Perceptron (MLP), Random Forest, and Logistic Regression classifiers are estimated and compared for the full and reduced feature sets. Decision Tree produced 28 rules in contrast with previous research work where 98002 rules had been generated from the same dataset with an accuracy rate of approximately 99.97%. The resulting rules of this study achieved a prediction accuracy for the histological staging of liver fibrosis of 97.45%. Among all the machine learning methods, MLP achieved the highest accuracy rate.